Best Holistic Adaptable Ranking of Attributes Technique-2 for Fuzzy Decision-Making in Industry 4.0: A Robust and Computationally Efficient Multi-Attribute Decision-Making Approach

Real-world decision-making in the Industry 4.0 era, such as the selection of logistics service providers and manufacturing partners, is increasingly influenced by uncertainty, vagueness, and imprecise data. Conventional fuzzy multi-attribute decision-making (MADM) approaches, including Fuzzy Analytic Hierarchy Process (AHP) and Fuzzy Technique for Order Preference by Similarity to Ideal Solution, often involve high computational complexity, extensive pairwise comparisons, and parameter dependency, which may limit their applicability in large-scale and time-sensitive decision environments. To address these limitations, this study proposes the Best Holistic Adaptable Ranking of Attributes Technique-2 (BHARAT-2), a computationally efficient framework for solving fuzzy MADM problems. In the proposed approach, fuzzy evaluations are processed directly within the BHARAT-2 framework during criteria weighting and composite score computation. Subsequently, the arithmetic mean is applied only to the final composite fuzzy scores for ranking the alternatives. Three real-world case studies are analyzed to demonstrate the applicability and effectiveness of the proposed framework. The obtained weights and rankings are compared with several established approaches, including equal weights, rank-inverse, rank-centroid, direct assignment, pairwise comparison, and AHP methods. The comparative results demonstrate strong agreement between BHARAT-2 and conventional techniques, with rank correlation values of 0.9762, 0.9000, and 0.8909-0.9909 for Case Studies 1, 2, and 3, respectively, indicating high consistency in decision outcomes. Although minor ranking variations are observed due to sensitivity to small weight changes, BHARAT-2 exhibits comparatively stable ranking behavior and lower variability across all case studies. Furthermore, the proposed framework offers reduced computational complexity and improved decision consistency while maintaining methodological simplicity and scalability. These characteristics make BHARAT-2 a reliable and efficient decision-support framework for complex decision-making applications in Industry 4.0, healthcare, banking, and related domains.